Overview

Dataset statistics

Number of variables12
Number of observations1928270
Missing cells1254288
Missing cells (%)5.4%
Duplicate rows1835
Duplicate rows (%)0.1%
Total size in memory255.7 MiB
Average record size in memory139.1 B

Variable types

Numeric9
Categorical3

Alerts

RND has constant value "0"Constant
Dataset has 1835 (0.1%) duplicate rowsDuplicates
FUEL_RATE is highly overall correlated with PAYLOADHigh correlation
PAYLOAD is highly overall correlated with FUEL_RATEHigh correlation
GPSELEVATION has 140290 (7.3%) missing valuesMissing
FUEL_RATE has 777687 (40.3%) missing valuesMissing
PAYLOAD has 324687 (16.8%) missing valuesMissing
GPSNORTHING is highly skewed (γ1 = -170.5008896)Skewed
GPSEASTING is highly skewed (γ1 = 92.30052561)Skewed
FUEL_RATE is highly skewed (γ1 = 110.4394249)Skewed
PAYLOAD has 1321626 (68.5%) zerosZeros
SHOVEL_ID has 59787 (3.1%) zerosZeros
DUMP_ID has 57984 (3.0%) zerosZeros

Reproduction

Analysis started2023-01-07 02:50:40.360592
Analysis finished2023-01-07 02:52:58.860116
Duration2 minutes and 18.5 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

TIMESTAMP
Real number (ℝ)

Distinct344331
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6492877 × 1012
Minimum1.6489441 × 1012
Maximum1.6496352 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.4 MiB
2023-01-06T18:52:58.997952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.6489441 × 1012
5-th percentile1.6489834 × 1012
Q11.6491168 × 1012
median1.6492854 × 1012
Q31.6494577 × 1012
95-th percentile1.6496008 × 1012
Maximum1.6496352 × 1012
Range6.91134 × 108
Interquartile range (IQR)3.409155 × 108

Descriptive statistics

Standard deviation1.9750718 × 108
Coefficient of variation (CV)0.00011975302
Kurtosis-1.1892155
Mean1.6492877 × 1012
Median Absolute Deviation (MAD)1.70426 × 108
Skewness0.029541098
Sum3.1802719 × 1018
Variance3.9009087 × 1016
MonotonicityNot monotonic
2023-01-06T18:52:59.306103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.649058202 × 101218
 
< 0.1%
1.649515774 × 101218
 
< 0.1%
1.649135874 × 101217
 
< 0.1%
1.649171878 × 101217
 
< 0.1%
1.649030988 × 101217
 
< 0.1%
1.64925337 × 101217
 
< 0.1%
1.649034 × 101217
 
< 0.1%
1.649011012 × 101217
 
< 0.1%
1.649357382 × 101217
 
< 0.1%
1.6495177 × 101217
 
< 0.1%
Other values (344321) 1928098
> 99.9%
ValueCountFrequency (%)
1.648944064 × 10121
< 0.1%
1.648944082 × 10121
< 0.1%
1.648944098 × 10122
< 0.1%
1.648944102 × 10121
< 0.1%
1.648944108 × 10121
< 0.1%
1.648944116 × 10121
< 0.1%
1.64894412 × 10121
< 0.1%
1.648944128 × 10121
< 0.1%
1.648944132 × 10121
< 0.1%
1.64894414 × 10121
< 0.1%
ValueCountFrequency (%)
1.649635198 × 10123
 
< 0.1%
1.649635196 × 10126
< 0.1%
1.649635194 × 10121
 
< 0.1%
1.649635192 × 10126
< 0.1%
1.64963519 × 10125
< 0.1%
1.649635188 × 10129
< 0.1%
1.649635186 × 10123
 
< 0.1%
1.649635184 × 10125
< 0.1%
1.649635182 × 10123
 
< 0.1%
1.64963518 × 10123
 
< 0.1%

GPSNORTHING
Real number (ℝ)

Distinct1919838
Distinct (%)99.9%
Missing5812
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean55829.379
Minimum-5978350
Maximum61239.654
Zeros0
Zeros (%)0.0%
Negative78
Negative (%)< 0.1%
Memory size29.4 MiB
2023-01-06T18:52:59.596849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-5978350
5-th percentile52937.355
Q155195.612
median56019.237
Q356975.25
95-th percentile59714.298
Maximum61239.654
Range6039589.7
Interquartile range (IQR)1779.6371

Descriptive statistics

Standard deviation33472.358
Coefficient of variation (CV)0.59954738
Kurtosis29917.148
Mean55829.379
Median Absolute Deviation (MAD)903.25452
Skewness-170.50089
Sum1.0732964 × 1011
Variance1.1203987 × 109
MonotonicityNot monotonic
2023-01-06T18:52:59.905298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5978350 53
 
< 0.1%
53282.36354 3
 
< 0.1%
52601.5525 3
 
< 0.1%
52962.29498 3
 
< 0.1%
52605.32465 3
 
< 0.1%
53121.5436 3
 
< 0.1%
54540.6416 2
 
< 0.1%
52597.50584 2
 
< 0.1%
55214.72796 2
 
< 0.1%
55760.79237 2
 
< 0.1%
Other values (1919828) 1922382
99.7%
(Missing) 5812
 
0.3%
ValueCountFrequency (%)
-5978350 53
< 0.1%
-2962544.489 1
 
< 0.1%
-2961017.207 1
 
< 0.1%
-2961015.688 1
 
< 0.1%
-2961007.422 1
 
< 0.1%
-2961001.963 1
 
< 0.1%
-2960996.355 1
 
< 0.1%
-2960992.46 1
 
< 0.1%
-2960987.422 1
 
< 0.1%
-2960986.554 1
 
< 0.1%
ValueCountFrequency (%)
61239.65421 1
< 0.1%
61235.91067 1
< 0.1%
61216.35964 1
< 0.1%
61188.98726 1
< 0.1%
61188.61279 1
< 0.1%
61187.15287 1
< 0.1%
61185.37001 1
< 0.1%
61184.25664 1
< 0.1%
61184.16772 1
< 0.1%
61178.07432 1
< 0.1%

GPSEASTING
Real number (ℝ)

Distinct1919910
Distinct (%)99.9%
Missing5812
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean227463.25
Minimum222469.51
Maximum671590.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.4 MiB
2023-01-06T18:53:00.187541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum222469.51
5-th percentile223765.92
Q1227413.32
median228135.24
Q3228640.15
95-th percentile229200.33
Maximum671590.38
Range449120.87
Interquartile range (IQR)1226.8313

Descriptive statistics

Standard deviation3020.5209
Coefficient of variation (CV)0.01327916
Kurtosis13239.328
Mean227463.25
Median Absolute Deviation (MAD)622.60358
Skewness92.300526
Sum4.3728855 × 1011
Variance9123546.6
MonotonicityNot monotonic
2023-01-06T18:53:00.463802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
671590.3795 53
 
< 0.1%
227576.0002 3
 
< 0.1%
227661.694 3
 
< 0.1%
227660.8086 3
 
< 0.1%
224534.7525 2
 
< 0.1%
227408.2111 2
 
< 0.1%
227536.8049 2
 
< 0.1%
228497.5337 2
 
< 0.1%
224737.8254 2
 
< 0.1%
227408.9431 2
 
< 0.1%
Other values (1919900) 1922384
99.7%
(Missing) 5812
 
0.3%
ValueCountFrequency (%)
222469.5098 1
< 0.1%
222470.6495 1
< 0.1%
222471.5773 1
< 0.1%
222471.9405 1
< 0.1%
222472.6591 1
< 0.1%
222472.6663 1
< 0.1%
222472.8266 1
< 0.1%
222472.9764 1
< 0.1%
222473.0126 1
< 0.1%
222473.0149 1
< 0.1%
ValueCountFrequency (%)
671590.3795 53
< 0.1%
449690.6376 1
 
< 0.1%
449670.9756 1
 
< 0.1%
449620.7566 1
 
< 0.1%
449618.6994 1
 
< 0.1%
449603.0126 1
 
< 0.1%
449602.2112 1
 
< 0.1%
449600.0127 1
 
< 0.1%
449593.6226 1
 
< 0.1%
449588.1352 1
 
< 0.1%

GPSELEVATION
Real number (ℝ)

Distinct15828
Distinct (%)0.9%
Missing140290
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean206.18528
Minimum-1218
Maximum682.9732
Zeros1
Zeros (%)< 0.1%
Negative435
Negative (%)< 0.1%
Memory size29.4 MiB
2023-01-06T18:53:00.683214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1218
5-th percentile92.0808
Q1135.807
median199.9144
Q3246.036
95-th percentile364.5474
Maximum682.9732
Range1900.9732
Interquartile range (IQR)110.229

Descriptive statistics

Standard deviation87.116756
Coefficient of variation (CV)0.42251685
Kurtosis1.3549084
Mean206.18528
Median Absolute Deviation (MAD)60.291
Skewness0.32506632
Sum3.6865516 × 108
Variance7589.3292
MonotonicityNot monotonic
2023-01-06T18:53:00.875939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103.124 1187
 
0.1%
103.2052 1172
 
0.1%
102.718 1152
 
0.1%
103.4488 1137
 
0.1%
102.9616 1135
 
0.1%
102.5556 1134
 
0.1%
102.6368 1130
 
0.1%
103.53 1129
 
0.1%
103.3676 1128
 
0.1%
102.4744 1113
 
0.1%
Other values (15818) 1776563
92.1%
(Missing) 140290
 
7.3%
ValueCountFrequency (%)
-1218 51
< 0.1%
-711.7992 1
 
< 0.1%
-665.7994 1
 
< 0.1%
-663.8912 1
 
< 0.1%
-659.8312 1
 
< 0.1%
-640.1402 1
 
< 0.1%
-634.0502 1
 
< 0.1%
-606.4016 1
 
< 0.1%
-605.143 1
 
< 0.1%
-578.6718 1
 
< 0.1%
ValueCountFrequency (%)
682.9732 1
< 0.1%
667.87 1
< 0.1%
666.5708 1
< 0.1%
616.7546 1
< 0.1%
613.6284 1
< 0.1%
527.2722 1
< 0.1%
520.7356 1
< 0.1%
502.9528 1
< 0.1%
494.1426 1
< 0.1%
493.29 1
< 0.1%

FUEL_RATE
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct28096
Distinct (%)2.4%
Missing777687
Missing (%)40.3%
Infinite0
Infinite (%)0.0%
Mean200.01969
Minimum196
Maximum1506.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.4 MiB
2023-01-06T18:53:01.112274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum196
5-th percentile196.2975
Q1196.5345
median197.4355
Q3204.8695
95-th percentile207.11
Maximum1506.7
Range1310.7
Interquartile range (IQR)8.335

Descriptive statistics

Standard deviation9.3396836
Coefficient of variation (CV)0.046693822
Kurtosis15376.383
Mean200.01969
Median Absolute Deviation (MAD)1.0845
Skewness110.43942
Sum2.3013925 × 108
Variance87.229691
MonotonicityNot monotonic
2023-01-06T18:53:01.510693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
206.239 24511
 
1.3%
196 4902
 
0.3%
196.476 928
 
< 0.1%
196.47 911
 
< 0.1%
206.233 897
 
< 0.1%
196.474 893
 
< 0.1%
196.471 891
 
< 0.1%
196.472 891
 
< 0.1%
196.468 891
 
< 0.1%
196.473 885
 
< 0.1%
Other values (28086) 1113983
57.8%
(Missing) 777687
40.3%
ValueCountFrequency (%)
196 4902
0.3%
196.0005 18
 
< 0.1%
196.001 77
 
< 0.1%
196.0015 26
 
< 0.1%
196.002 95
 
< 0.1%
196.0025 28
 
< 0.1%
196.003 86
 
< 0.1%
196.0035 22
 
< 0.1%
196.004 98
 
< 0.1%
196.0045 16
 
< 0.1%
ValueCountFrequency (%)
1506.7 46
< 0.1%
851.4305 1
 
< 0.1%
851.3505 1
 
< 0.1%
851.35 1
 
< 0.1%
260.255 1
 
< 0.1%
228.1275 1
 
< 0.1%
215.7585 1
 
< 0.1%
215.552 1
 
< 0.1%
215.3425 1
 
< 0.1%
215.137 1
 
< 0.1%

STATUS
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.4 MiB
NON_PRODUCTIVE
669920 
Hauling
606644 
Empty
427149 
Truck Loading
91686 
Queue At LU
 
61360
Other values (4)
71511 

Length

Max length24
Median length15
Mean length9.5213139
Min length5

Characters and Unicode

Total characters18359664
Distinct characters36
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEmpty
2nd rowEmpty
3rd rowEmpty
4th rowEmpty
5th rowEmpty

Common Values

ValueCountFrequency (%)
NON_PRODUCTIVE 669920
34.7%
Hauling 606644
31.5%
Empty 427149
22.2%
Truck Loading 91686
 
4.8%
Queue At LU 61360
 
3.2%
Dumping 30013
 
1.6%
Spot at LU 21357
 
1.1%
Queuing at Dump 19488
 
1.0%
Wenco General Production 653
 
< 0.1%

Length

2023-01-06T18:53:01.733321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-06T18:53:01.976089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
non_productive 669920
30.1%
hauling 606644
27.3%
empty 427149
19.2%
at 102205
 
4.6%
truck 91686
 
4.1%
loading 91686
 
4.1%
lu 82717
 
3.7%
queue 61360
 
2.8%
dumping 30013
 
1.3%
spot 21357
 
1.0%
Other values (5) 40935
 
1.8%

Most occurring characters

ValueCountFrequency (%)
N 1339840
 
7.3%
O 1339840
 
7.3%
E 1097069
 
6.0%
u 910180
 
5.0%
T 761606
 
4.1%
U 752637
 
4.1%
n 749790
 
4.1%
i 748484
 
4.1%
g 747831
 
4.1%
a 739828
 
4.0%
Other values (26) 9172559
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10306584
56.1%
Lowercase Letter 7085758
38.6%
Connector Punctuation 669920
 
3.6%
Space Separator 297402
 
1.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 1339840
13.0%
O 1339840
13.0%
E 1097069
10.6%
T 761606
7.4%
U 752637
7.3%
D 719421
7.0%
P 670573
6.5%
I 669920
6.5%
V 669920
6.5%
C 669920
6.5%
Other values (8) 1615838
15.7%
Lowercase Letter
ValueCountFrequency (%)
u 910180
12.8%
n 749790
10.6%
i 748484
10.6%
g 747831
10.6%
a 739828
10.4%
l 607297
8.6%
t 551364
7.8%
p 498007
7.0%
m 476650
6.7%
y 427149
6.0%
Other values (6) 629178
8.9%
Connector Punctuation
ValueCountFrequency (%)
_ 669920
100.0%
Space Separator
ValueCountFrequency (%)
297402
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17392342
94.7%
Common 967322
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 1339840
 
7.7%
O 1339840
 
7.7%
E 1097069
 
6.3%
u 910180
 
5.2%
T 761606
 
4.4%
U 752637
 
4.3%
n 749790
 
4.3%
i 748484
 
4.3%
g 747831
 
4.3%
a 739828
 
4.3%
Other values (24) 8205237
47.2%
Common
ValueCountFrequency (%)
_ 669920
69.3%
297402
30.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18359664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 1339840
 
7.3%
O 1339840
 
7.3%
E 1097069
 
6.0%
u 910180
 
5.0%
T 761606
 
4.1%
U 752637
 
4.1%
n 749790
 
4.1%
i 748484
 
4.1%
g 747831
 
4.1%
a 739828
 
4.0%
Other values (26) 9172559
50.0%

PAYLOAD
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1344
Distinct (%)0.1%
Missing324687
Missing (%)16.8%
Infinite0
Infinite (%)0.0%
Mean46.576665
Minimum0
Maximum634.101
Zeros1321626
Zeros (%)68.5%
Negative0
Negative (%)0.0%
Memory size29.4 MiB
2023-01-06T18:53:02.226206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile311.974
Maximum634.101
Range634.101
Interquartile range (IQR)0

Descriptive statistics

Standard deviation104.73515
Coefficient of variation (CV)2.2486615
Kurtosis2.0900349
Mean46.576665
Median Absolute Deviation (MAD)0
Skewness1.9441091
Sum74689548
Variance10969.452
MonotonicityNot monotonic
2023-01-06T18:53:02.471724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1321626
68.5%
270.439 2213
 
0.1%
262.132 2190
 
0.1%
312.897 2133
 
0.1%
274.131 2060
 
0.1%
315.666 2044
 
0.1%
287.053 1997
 
0.1%
319.358 1996
 
0.1%
263.978 1981
 
0.1%
311.974 1967
 
0.1%
Other values (1334) 263376
 
13.7%
(Missing) 324687
 
16.8%
ValueCountFrequency (%)
0 1321626
68.5%
12.922 31
 
< 0.1%
16.614 19
 
< 0.1%
17.537 68
 
< 0.1%
28.613 73
 
< 0.1%
30.0898 70
 
< 0.1%
30.1821 43
 
< 0.1%
34.151 59
 
< 0.1%
38.9506 62
 
< 0.1%
44.0271 63
 
< 0.1%
ValueCountFrequency (%)
634.101 42
< 0.1%
629.486 65
< 0.1%
503.035 66
< 0.1%
472.576 19
 
< 0.1%
443.963 24
 
< 0.1%
435.656 31
< 0.1%
425.503 25
 
< 0.1%
423.657 17
 
< 0.1%
422.734 55
< 0.1%
418.119 21
 
< 0.1%

TRUCK_ID
Real number (ℝ)

Distinct70
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.589543
Minimum0
Maximum69
Zeros15314
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size29.4 MiB
2023-01-06T18:53:02.699709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q117
median32
Q348
95-th percentile61
Maximum69
Range69
Interquartile range (IQR)31

Descriptive statistics

Standard deviation18.490044
Coefficient of variation (CV)0.56736124
Kurtosis-1.1522754
Mean32.589543
Median Absolute Deviation (MAD)16
Skewness-0.0074349166
Sum62841438
Variance341.88171
MonotonicityNot monotonic
2023-01-06T18:53:02.947253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 35335
 
1.8%
7 34998
 
1.8%
1 34509
 
1.8%
42 34365
 
1.8%
46 34271
 
1.8%
40 34210
 
1.8%
30 34122
 
1.8%
26 34069
 
1.8%
50 34018
 
1.8%
52 33795
 
1.8%
Other values (60) 1584578
82.2%
ValueCountFrequency (%)
0 15314
0.8%
1 34509
1.8%
2 18077
0.9%
3 32448
1.7%
4 22344
1.2%
5 29628
1.5%
6 19329
1.0%
7 34998
1.8%
8 27674
1.4%
9 33251
1.7%
ValueCountFrequency (%)
69 997
 
0.1%
68 3551
 
0.2%
67 5472
 
0.3%
66 9747
 
0.5%
65 12143
 
0.6%
64 23629
1.2%
63 22932
1.2%
62 4656
 
0.2%
61 30620
1.6%
60 26931
1.4%

TRUCK_TYPE_ID
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.4 MiB
3
1225307 
4
248601 
1
246309 
2
179599 
0
 
28454

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1928270
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
3 1225307
63.5%
4 248601
 
12.9%
1 246309
 
12.8%
2 179599
 
9.3%
0 28454
 
1.5%

Length

2023-01-06T18:53:03.162544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-06T18:53:03.401971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
3 1225307
63.5%
4 248601
 
12.9%
1 246309
 
12.8%
2 179599
 
9.3%
0 28454
 
1.5%

Most occurring characters

ValueCountFrequency (%)
3 1225307
63.5%
4 248601
 
12.9%
1 246309
 
12.8%
2 179599
 
9.3%
0 28454
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928270
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1225307
63.5%
4 248601
 
12.9%
1 246309
 
12.8%
2 179599
 
9.3%
0 28454
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1928270
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1225307
63.5%
4 248601
 
12.9%
1 246309
 
12.8%
2 179599
 
9.3%
0 28454
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1928270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1225307
63.5%
4 248601
 
12.9%
1 246309
 
12.8%
2 179599
 
9.3%
0 28454
 
1.5%

SHOVEL_ID
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.594173
Minimum0
Maximum8
Zeros59787
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size29.4 MiB
2023-01-06T18:53:03.599319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q36
95-th percentile7
Maximum8
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1758507
Coefficient of variation (CV)0.60538285
Kurtosis-1.1306948
Mean3.594173
Median Absolute Deviation (MAD)2
Skewness0.22467457
Sum6930536
Variance4.7343262
MonotonicityNot monotonic
2023-01-06T18:53:03.833537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 421705
21.9%
3 365153
18.9%
4 283043
14.7%
6 261306
13.6%
7 223372
11.6%
2 169937
8.8%
5 113950
 
5.9%
0 59787
 
3.1%
8 30017
 
1.6%
ValueCountFrequency (%)
0 59787
 
3.1%
1 421705
21.9%
2 169937
8.8%
3 365153
18.9%
4 283043
14.7%
5 113950
 
5.9%
6 261306
13.6%
7 223372
11.6%
8 30017
 
1.6%
ValueCountFrequency (%)
8 30017
 
1.6%
7 223372
11.6%
6 261306
13.6%
5 113950
 
5.9%
4 283043
14.7%
3 365153
18.9%
2 169937
8.8%
1 421705
21.9%
0 59787
 
3.1%

DUMP_ID
Real number (ℝ)

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5224201
Minimum0
Maximum35
Zeros57984
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size29.4 MiB
2023-01-06T18:53:04.058947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q39
95-th percentile22
Maximum35
Range35
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.5940921
Coefficient of variation (CV)1.0109886
Kurtosis2.4710866
Mean6.5224201
Median Absolute Deviation (MAD)2
Skewness1.5523586
Sum12576987
Variance43.482051
MonotonicityNot monotonic
2023-01-06T18:53:04.296790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1 505219
26.2%
9 333750
17.3%
3 244678
12.7%
2 179596
 
9.3%
11 116824
 
6.1%
5 76375
 
4.0%
7 69795
 
3.6%
0 57984
 
3.0%
15 45769
 
2.4%
6 41973
 
2.2%
Other values (26) 256307
13.3%
ValueCountFrequency (%)
0 57984
 
3.0%
1 505219
26.2%
2 179596
 
9.3%
3 244678
12.7%
4 1031
 
0.1%
5 76375
 
4.0%
6 41973
 
2.2%
7 69795
 
3.6%
8 7954
 
0.4%
9 333750
17.3%
ValueCountFrequency (%)
35 880
 
< 0.1%
34 1993
 
0.1%
33 279
 
< 0.1%
32 12472
0.6%
31 1092
 
0.1%
30 199
 
< 0.1%
29 6624
 
0.3%
28 9021
0.5%
27 130
 
< 0.1%
26 20532
1.1%

RND
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.4 MiB
0
1928270 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1928270
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1928270
100.0%

Length

2023-01-06T18:53:04.526973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-06T18:53:04.734433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1928270
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1928270
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928270
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1928270
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1928270
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1928270
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1928270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1928270
100.0%

Interactions

2023-01-06T18:52:42.107771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:51:47.283749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:51:54.637814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:01.691808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:08.848376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:15.863200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:21.514802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:28.077462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:35.188692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:42.851399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:51:48.322791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:51:55.410808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:02.509831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:09.658295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:16.448010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:22.254826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:28.892660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:36.035198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:43.695893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:51:49.169863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:51:56.164279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:03.295733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:10.472093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:17.021359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:22.954055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:29.750105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:36.920530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:44.618442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:51:49.990551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:51:56.889548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:04.006903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:11.254572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:17.603848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:23.671451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:30.590046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:37.723800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:45.237954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:51:50.642354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:51:57.533959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:04.625397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:11.854652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:18.225092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:24.256686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:31.331685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:38.330021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:45.997431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:51:51.382935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:51:58.219891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:05.272493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:12.605569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:18.945191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:24.978290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:32.147743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:39.138426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:46.823656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:51:52.152236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:51:59.103789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:06.200289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:13.418561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:19.565743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:25.694304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:32.931920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:39.933481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:47.509422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:51:53.034206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:51:59.951572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:07.082222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:14.288679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:20.186121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:26.489687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:33.641578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:40.655364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:48.271414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:51:53.792154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:00.797757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:08.016858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:15.199198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:20.786469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:27.311514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:34.422323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T18:52:41.355003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-06T18:53:04.861717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
TIMESTAMPGPSNORTHINGGPSEASTINGGPSELEVATIONFUEL_RATEPAYLOADTRUCK_IDSHOVEL_IDDUMP_IDSTATUSTRUCK_TYPE_ID
TIMESTAMP1.000-0.0320.037-0.0450.027-0.0620.0980.1120.0480.0220.040
GPSNORTHING-0.0321.0000.319-0.1250.017-0.133-0.0750.0060.3790.0040.001
GPSEASTING0.0370.3191.000-0.443-0.0260.047-0.0090.1110.1580.0040.001
GPSELEVATION-0.045-0.125-0.4431.0000.088-0.040-0.024-0.128-0.0930.1130.123
FUEL_RATE0.0270.017-0.0260.0881.0000.587-0.011-0.024-0.0030.0080.002
PAYLOAD-0.062-0.1330.047-0.0400.5871.0000.042-0.182-0.0590.3530.160
TRUCK_ID0.098-0.075-0.009-0.024-0.0110.0421.000-0.060-0.0090.0290.334
SHOVEL_ID0.1120.0060.111-0.128-0.024-0.182-0.0601.0000.2470.0530.386
DUMP_ID0.0480.3790.158-0.093-0.003-0.059-0.0090.2471.0000.0320.180
STATUS0.0220.0040.0040.1130.0080.3530.0290.0530.0321.0000.036
TRUCK_TYPE_ID0.0400.0010.0010.1230.0020.1600.3340.3860.1800.0361.000

Missing values

2023-01-06T18:52:48.931750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-06T18:52:51.258748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-06T18:52:55.644503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

TIMESTAMPGPSNORTHINGGPSEASTINGGPSELEVATIONFUEL_RATESTATUSPAYLOADTRUCK_IDTRUCK_TYPE_IDSHOVEL_IDDUMP_IDRND
2164894406400055334.328779224524.094341402.9144NaNEmpty0.000000
11164894408200055250.890875224568.985459402.3460NaNEmpty0.000000
19164894409800055236.506152224491.232652400.4378NaNEmpty0.000000
21164894410200055252.532868224465.523704398.0018NaNEmpty0.000000
24164894410800055288.883906224411.149771392.3178NaNEmpty0.000000
40164894414000055514.308992224539.611022374.6974NaNEmpty0.000000
44164894414800055573.410229224622.415133370.7186NaNEmpty0.000000
47164894415400055605.646336224663.560834366.6992NaNEmpty0.000000
71164894420200056164.526453224993.898258357.1176NaNEmpty0.000000
104164894426800056634.574606225308.730247352.0426NaNEmpty0.000000
TIMESTAMPGPSNORTHINGGPSEASTINGGPSELEVATIONFUEL_RATESTATUSPAYLOADTRUCK_IDTRUCK_TYPE_IDSHOVEL_IDDUMP_IDRND
19288039164895019000060412.696940228647.729980306.4488202.0100HaulingNaN323440
19288054164895022000060700.782780228465.097398307.8292197.8280HaulingNaN323440
19288062164895023600060823.435953228544.015772305.9210196.0000HaulingNaN323440
19288068164895024800060910.504685228612.216075305.6368199.3025HaulingNaN323440
19288070164895025200060934.985679228630.394462305.8804201.3795HaulingNaN323440
19288079164895027000061079.573587228724.914300305.1496197.8905HaulingNaN323440
19288088164895028800061235.910674228794.566803306.5706197.0890HaulingNaN323440
19288090164895029200061239.654205228814.677157306.9766199.4595HaulingNaN323440
19288093164895029800061216.359638228834.084380306.8548205.1040HaulingNaN323440
19288129164895036800061135.857382228851.565018308.7630196.0000Dumping0.0323440

Duplicate rows

Most frequently occurring

TIMESTAMPGPSNORTHINGGPSEASTINGGPSELEVATIONFUEL_RATESTATUSPAYLOADTRUCK_IDTRUCK_TYPE_IDSHOVEL_IDDUMP_IDRND# duplicates
1695164958950600052962.294981227576.000227232.6380NaNNON_PRODUCTIVE0.000742103
1748164959278600052605.324649227660.808637242.2602NaNNON_PRODUCTIVE0.000742103
1781164959545000052601.552504227661.693957245.9142NaNNON_PRODUCTIVE0.000742103
0164894843800058718.439945228642.744231101.6218196.4525NON_PRODUCTIVE0.000534702
1164894883000056454.550604228973.06317095.7348NaNNON_PRODUCTIVE0.0001921202
2164894929800055205.574332223465.386117359.9596196.4110NON_PRODUCTIVE0.0001233502
3164895367600058778.375051228590.84883187.8584196.5755Empty0.0003234902
4164895447800056297.674071228984.53634887.2900NaNEmpty0.0003431102
5164895461800058768.841177228586.24994193.7860NaNEmpty0.0002034902
6164895931200053161.757250227654.566913230.5674NaNHauling338.7417421102